Non-intrusive Power Management

ABSTRACT

A method and system for managing power consumption of a pool of computing devices that are logically grouped to provide a common set of functionality is disclosed. One aspect of certain embodiments includes predicting resource utilization for each device without installing customized software, firmware or hardware on the device.

TECHNICAL FIELD

The disclosed embodiments relate generally to power management in anypool of computing devices that are logically grouped to provide a commonset of functionality. More particularly, the disclosed embodimentsrelate to power management in server pools, interchangeably referred toas server clusters, typically found in large computing establishmentslike data centers.

BACKGROUND

The proliferation of the Internet, devices that access it, andconsequently, Internet based services are driving an insatiable thirstfor computational power. To meet this need, large data centers have beenset up. Typical data centers house hundreds, maybe even thousands ofservers, and serve as the backbone for a variety of Internet services.The services hosted by data centers typically come with the requirementof high availability, close to 99.9% up time, which is usually supportedby replicating servers and maintaining spare capacity. Furthermore, datacenters are designed for a peak loads which are both occasional andshort lived. As a result, data centers tend to consume large amounts ofpower. In phases that the data center is not fully loaded, idle serverscan be shutdown without substantial loss in performance. When the loadincreases, powered off servers can be booted-up to service the requestsand maintain Quality of Service (QoS).

Reducing the power consumption of a data center contributes to reducedoperational expense, and allows the data center operator to invest innewer hardware and supporting infrastructure, to save money and/or toprovide improved services to customers. Prior studies have reported thatservers can draw close to 60% of their peak power consumption when idle,and that the global electricity costs for data centers have beenreported as running into the billions. Therefore, substantial reductionin power consumption can be achieved by shutting down idle senders.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the aspects of the invention as well asembodiments thereof, reference should be made to the description ofembodiments below, in conjunction with the following drawings in whichlike reference numerals refer to corresponding parts throughout thefigures.

FIG. 1 is a high-level block diagram illustrating power management of apool of computing devices that are logically grouped to provide a commonset of functionality, according to certain embodiments of the invention.

FIG. 2 is a block diagram showing some of the high-level steps forobtaining correlation information associated with the servers in theserver pool, according to certain embodiments of the invention.

FIG. 3 is a block diagram that illustrates a power management method,according to certain embodiments of the invention.

FIG. 4 illustrates the class diagram of the central classes used forimplementing the power manager, according to certain embodiments of theinvention.

FIG. 5 illustrates the class diagram for the LoadInformation classhierarchy, according to certain embodiments of the invention.

FIG. 6 illustrates the class diagram for the UtilizatonPredictor classhierarchy, according to certain embodiments of the invention.

FIG. 7 illustrates the class diagram for the ResourcesMeasureMethodclass hierarchy, according to certain embodiments of the invention.

FIG. 8 illustrates the class diagram for the LoadBalancer classhierarchy, according to certain embodiments of the invention.

DESCRIPTION OF EMBODIMENTS

Methods, systems and other aspects of the invention are described.Reference will be made to certain embodiments of the invention, examplesof which are illustrated in the accompanying drawings. While theinvention will be described in conjunction with the embodiments, it willbe understood that it is not intended to limit the invention to theseparticular embodiments alone. On the contrary, the invention is intendedto cover alternatives, modifications and equivalents that are within thespirit and scope of the invention. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense.

Moreover, in the following description, numerous specific details areset forth to provide a thorough understanding of the present invention.However, it will be apparent to one of ordinary skill in the art thatthe invention may be practiced without these particular details. Inother instances, methods, procedures, components, and networks that arewell known to those of ordinary skill in the art are not described indetail to avoid obscuring aspects of the present invention.

The embodiments described herein are in reference to servers in datacenters. However, the embodiments apply to any pool of computing devicesthat are logically grouped to provide a common set of functionality.

According to certain embodiments, the problem associated with powerconsumption in data centers can be effectively managed by turning off orturning on servers in response to the load experienced by the datacenter. Servers are turned on when the load increases and turned offwhen load decreases. Load can be defined by the number and/or size ofrequests that are being received by the server pool per unit timeinterval, for example.

According to certain embodiments, a new server or device ischaracterized to understand how the resource utilization changes as thenumber of requests being serviced changes by the server/device. Thecharacterization, using statistical analysis techniques, can be used topredict the utilization of the server/device for a given load. Thecorrelation function associated with the characterization is stored in adatabase, for example. A power management server can retrieve thecorrelation function during initialization. The power management servertakes decisions at regular time intervals to shutdown a server/device,power-on a server/device or maintain status quo in the pool ofservers/devices based on the predicted utilization.

According to certain embodiments, a non-intrusive mechanism is used topower down servers or devices. In contrast, existing power managementsolutions typically require that the data center operators installsoftware, firmware or hardware on the servers/devices for powermanagement. Such power management decisions are taken by a centralizedadministrative software component which communicates with the softwareinstalled in the individual servers, which then initiate the action. Thecustom software typically sends information that the centralized entitycan use for decision making. Such an approach is intrusive unlike thenon-intrusive approach as described in the embodiments herein. Theembodiments described herein do not require any such additionalsoftware, firmware or hardware installation on each server/device in thedata center.

According to certain embodiments, a centralized entity takes powermanagement decisions and initiates them on the servers/devices withoutthe need for custom software, hardware or firmware. The centralizedentity uses information exported by the OS only of the servers/devices.Such an approach requires little or no downtime for installation, doesnot require custom software to be installed, or require any major systemreconfiguration.

Further, unlike vendor specific solutions, the embodiments are notrestricted to hardware vendors (processor or OEM) or to operatingsystems.

FIG. 1 is a high-level block diagram illustrating power management of apool of computing devices that are logically grouped to provide a commonset of functionality, such as servers in a data center, according tocertain embodiments of the invention. In FIG. 1, system 100 includes anapplication delivery controller 104 that receives HTTP requests 102 fromclient devices, and a computer 106 that executes the power manager.Application delivery controller 104 sends the HTTP requests 102 to theserver pool 108 and also receives the responses to the HTTP requestsfrom server pool 108. The power manager implemented in computer 106receives information from application delivery controller 104 andinformation from the server pool 108 to make power management decisions.The power manager may be implemented on multiple computers as in adistributed computer system, according to certain embodiments.Application delivery controller 104 may be a commercial off-the-shelfload balancer, according to certain embodiments. Similarly, computer 106can be an off-the-shelf computer on which the power management solutionis installed and executes. Server pool 108 or server cluster comprisesserver machines or nodes that service requests from client devices viaapplication delivery controller 104. An application delivery controlleris hardware or software that manages requests received from clientdevices and distributes such requests to the computing devices in theserver pool. A non-limiting example of an application deliverycontroller is a load balancer.

HTTP requests initiated by client devices reach application deliverycontroller 104 which redirects the requests to an appropriate server inthe server pool 108. According to certain embodiments, applicationdelivery controller 104 is configured to use a round robin policy.Consequently, server nodes in server pool 108 service a comparablenumber of requests. The power manager interacts with applicationdelivery controller 104 to obtain information including but not limitedto:

-   -   Information on the number of requests being executed by each        server in server pool 108,    -   the average response time by each server in server pool 108, and    -   information on server state.

The power manager does not service any requests from client devices. Thepower manager's job is to make power management decisions and initiatesuch decisions, while maintaining consistency between actual serverstate and information at application delivery controller 104.

According to one aspect of certain embodiments, each server of at leasta subset of servers in the server pool is characterized for theutilization behaviour of that particular server. Characterizationinvolves measuring on the server to be characterized, the utilization ofvarious resources as the number of requests being executed by the servervaries. Such measurement information is utilized to draw correlationsbetween the number of requests being serviced by the server that isbeing characterized and its utilization of resources, according tocertain embodiments. The power manager (computer 106) can remotely querythe servers in server pool 108 to obtain resource utilizationinformation using standardized protocols like Simple Network ManagementProtocol (SNMP) for any OS or Windows Management Instrumentation (WMI)for MS Windows. The correlation drawn can be used to predict theutilization of a given server for any given number of HTTP requestsbeing serviced per minute, according to certain embodiments. Accordingto certain embodiments, the characterization is performed using the sameapplication that the server to be characterized is expected to executein production because a server can be expected to show differences inbehaviour with different application types.

According to certain embodiments, correlation information is obtainedusing well established statistical analysis techniques such as linearregression. The statistical analysis can be performed using anycommercially/freely available statistical analysis software such as Rstatistical software. According to certain embodiments, the correlationinformation is an expression that correlates the number of requests tothe CPU utilization. According to some embodiments, this correlationinformation is XML serialized and inserted into a database along withother information that the power management solution requires. XMLserialization is the process of converting a binary object in memoryinto an XML representation that can then be stored on disk (files ordatabase). For purposes of simplicity, the statistical analysis is donein the background and the results are stored in the database. Theprocess of deriving correlations can be made real time, according tocertain embodiments.

FIG. 2 is a block diagram showing some of the high-level steps forobtaining correlation information associated with the servers in theserver pool, according to certain embodiments of the invention. At block202, a fixed workload is executed against a given server that is to becharacterized. At block 204, the information on resource utilization,workload and other related information is logged for analysis. At block206, statistical analysis is performed on the information to obtaincorrelation information. At block 208, the correlation information forthe given server is stored in the database. If the database alreadycontains correlation information for the particular server, then thecorrelation information is updated. At block 210, the power managerretrieves correlation information for making power management decisions.

The power manager runs at regular intervals. For example, the powermanager can run every 10 seconds. At each iteration of the power managersolution, a decision is taken as to whether a server needs to be poweredon or powered off. The power manager also identifies which server mustbe powered on or off based on a server selection policy. The serverselection policy is described herein.

FIG. 3 is a block diagram that illustrates the power management method,according to certain embodiments of the invention. After initializationat block 302, correlation data is retrieved from the database at block304. At block 308, on each iteration, the power manager checks if allthe servers in the server pool are above a pre-configured utilizationthreshold called the overload threshold, according to certainembodiments. According to certain other embodiments, the utilizationthreshold is determined dynamically rather than being pre-configured. Ifall the servers are above the utilization threshold, then at block 310,the power manager determines if all the servers in the server pool arepowered on. If all the servers are powered on, then at block 306, thestatus quo of the server pool is maintained. If not all servers in theserver pool are powered on, then at block 314, the power manageridentifies which server is to be powered on, if more than one server isnot powered on in the server pool. At block 316, the power managerinitiates power-on process for the selected server. At block 318, thepower manager waits for the resume duration. At block 320, the powermanager updates the state information for the selected server that wasjust powered on. At block 322, the server that was just powered on ismarked on the application delivery controller as available for servicingrequests.

If at block 308, it is determined that not all the servers in the serverpool are below the utilization threshold, then at block 312 a check ismade to identify if any server in the server pool can be powered offsafely. If none of the servers in the server pool can be powered offsafely, then the status quo is maintained at block 334.

If there are servers in the server pool can be powered off, then atblock 324, the power manager identifies a server to be powered off. Theserver identified to be powered off is referred to as a candidateserver. A decision to power off is taken only if the load on thecandidate server can be migrated to the remaining power-on servers inthe server pool without causing such remaining power-on servers to crossan overload threshold associated with a given server. At block 326, theserver identified to be powered off is marked as unavailable on theapplication delivery controller. At block 328, the state information ofthe server to be powered off is updated. At block 330, the power managerwaits for the number of requests sent to the server to be powered offdrops to zero. At block 332, the power manager initiates the power-offprocess for the server to be powered off.

Powering servers on or off can be done using existing mechanismssupported by operating systems of the servers. For example, WindowsManagement Instrumentation (WMI) on Microsoft Windows or ssh basedremote command execution on Linux/Solaris can be used for poweringservers on or off.

According to certain embodiments, a staggered suspend and boot upprocess is used at a given point in time. In other words, exactly oneserver is suspending or resuming at a given time. The staggered suspendensures that there is capacity in the server pool to handle any spikesin the load and thus is a conservative approach. Staggered resumeensures that the load on the power supply for the server does not gohigh because computers typically draw higher power during the boot upphase.

According to certain embodiments, the power management method caninclude the following features:

-   -   Predicting the demand: Historical data can be analysed to        predict the demand that the server pool will experience in the        next time interval. The prediction can augment the decisions        taken by the power manager. Existing statistical methods like        Auto Regressive Moving Average can be used for the time based        trend analysis and prediction.    -   Predict the number of servers or devices required to support a        given workload.    -   Chart the response time and performance of a server or a device        under a given workload.    -   Moving server nodes across pools: The power management method        described herein can be extended to multiple pools using a        global power management scheme. In such a global power        management scheme, it is possible to move servers across pools        to serve the needs of various pools. Depending on the demand,        servers can be either moved across pools or turned on/off.

The Advanced Configuration and Power Interface (ACPI) specificationdefines the following server states, according to certain embodiments.Other suitable standards for defining server states can also be used.The embodiments are not restricted to the ACPI standard.

TABLE 1 ACPI Server States Server Global state State Description S0 G0Server is powered on and operational. S1 and S2 G1 Undefined and unused.S3 G1 Suspended to RAM—Operating system context stored in RAM and mostcomponents powered down. Typically RAM and NIC are active in this state.S4 G1 Suspend to Disk—Operating system context is written to disk andserver is powered down. S5 G2 Soft off—Server is powered down, no OScontext is retained. S5 G3 Mechanical off—Server is powered down andmain power supply is cut off.According to certain embodiments, servers are switched between S0 andS5.

If all the servers in the server pool have similar properties likeoperating frequency, RAM, disk space etc, the choice of server toshutdown/resume become trivial because any server can be chosen.However, if the server pools are heterogeneous pools, where serversdiffer in their properties, then a server selection policy is needed inorder to select an appropriate server to power on or off. According tocertain embodiments, policies that can be used to select servers ifmultiple servers are available for shutdown/resume are described below:

Polices for server power off include but are not limited to:

-   -   1. Lowest Frequency: Power off the server that operates at the        lowest frequency.    -   2. Highest power: Power off the server that consumes the highest        power.    -   3. Max post-utilization: Power off the server that will result        in other servers having high utilization.    -   4. Types of applications running on the system (application        capabilities).

The policies for server power on include but are not limited to:

-   -   1. Lowest power: Power on the server that consumes lowest power.    -   2. Highest frequency: Power on the server that runs at the        highest frequency.    -   3. Shortest Resume Time: Power on the server that takes the        shortest time to boot up.

As a non-limiting example, suspend policy 3 (max post-utilization) andresume policy 2 (highest frequency) can be used, according to certainembodiments. It is possible to support any combination of policies, butthe power management mechanism must ideally be configured to use theones that provide high power savings without significant loss inperformance. Further, different combinations of suspend and resumepolicies will show different power/performance characteristics.

At any point in time, at least one server will be active in the pool.The reasoning behind having at least one server active is to haveavailable computational capacity to handle requests while other serversare resuming.

As a non-limiting example, turning servers off is achieved by issuing aremote shutdown command using WMI (as our cluster is currently MSWindows based). Remote command execution requires that appropriateservices are enabled on the server and appropriate ports are kept on inthe firewall. Alternate techniques can be used for Linux and Solaris.Servers are turned on using Wake-On-LAN (WoL), an industry standardtechnique to resume computers that are currently suspended. A WoL packetis a specially crafted network packet which contains a WoL header andthe MAC address of the target server repeated 16 times. WoL packetdefinition is standardized. WoL must be supported by the networkinterface card (NIC) and also enabled by the operating system driver.Modern NICs typically support WoL.

Such a non-intrusiveness approach does not require any additionalsoftware components to be installed on the individual servers in theserver pool for the power manager to work. At most, it requires certainstandard operating system services which might be turned off by default(like ssh, snmp) to be turned on.

FIG. 4 illustrates the class diagram of the central classes used forimplementing the power manager, according to certain embodiments. FIG. 4shows ServerMachine class 402, ServerLoadInformation class 404, Resourceclass 406, ResourceMeasureMethod class 408, ImmutableServerPropertiesclass 410, UtilizationPredictor class 412, NetworkResource class 414,DiskResource class 416, and CPUResource class 418. The central datastructure to the application is a ServerMachine class 402 that holdsinformation about a server in the server cluster. The ServerMachineclass contains the immutable server properties (like MAC address,maximum operating frequency, power consumption etc) and dynamicallycreated objects for measuring resource utilization (see FIG. 7),predicting the utilization (FIG. 6), storing load information (FIG. 5)etc. A server contains resource objects—CPU, Disk, network, and memory,and is a resource in itself. The utilization predictor for each serveris read from on disk storage (a database, for example) as an XMLserialized stream and then de-serialized to get the object.

Some of the hierarchies of other classes used in the implementation ofthe power manager are described herein with reference to FIGS. 5-8.

FIG. 5 illustrates the class diagram for the LoadInformation classhierarchy, according to certain embodiments. LoadInformation classdefines classes that are used to store information onconnections/requests queried at regular intervals from the load balanceror server. FIG. 5 shows that LoadInformation class 502 includesLocalHTTPLoad Info class 504, PoolLoad Information class 506, andServerLoadInformation class 508. ServerLoadInformation class 508includes VirtualServerLoadInformation class.

FIG. 6 illustrates the class diagram for the UtilizationPredictor classhierarchy, according to certain embodiments. UtilizationPredictor class602 includes LinearRegressionBased class 604.

FIG. 7 illustrates the class diagram for the ResourcesMeasureMethodclass hierarchy, according to certain embodiments.ResourcesMeasureMethod class 702 includes WMIAdaptor class 704 andSNMPAdaptor class 706.

FIG. 8 illustrates the class diagram for the LoadBalancer classhierarchy, according to certain embodiments. LoadBalancer class 802includes F5Adaptor class 804. The load balancer class hierarchy is usedto define classes that can be used to query and control the loadbalancer.

According to certain embodiments, a simple database with a single tableis used to store information about individual servers.

The characterization phase requires utilization information to begathered from servers for later analysis. According to certainembodiments, this information is stored in a database. The utilizationinformation of each resource is stored in a separate file with theformat shown in Table 2 Utilization Information, as non-limitingexample.

TABLE 2 Utilization information Date-Time stamp Resource utilizationWeighted Moving average (varying from 0-100%) utilization (0-100%)

The weighted moving average is used to help smoothing any sharpfluctuations in the measured utilization. An example for CPU utilizationon a dual core machine, measured using WMI is given below.

Date-Time Core 0 Core 1 Total Moving Moving Moving stamp Avg Avg Avg(Total) (Core 0) (Core)

The level of detail—per core utilization—is not provided by SNMPimplementations. However, overall system utilization is available andthe power manager implementation uses the overall utilization foranalysis and decision making.

1. A method of power management, the method comprising: predictingresource utilization associated with each device from at least a subsetof devices without installing customized software, firmware or hardwareon the device for predicting the resource utilization; and selecting acandidate device from the subset of devices for powering on or off basedon the predicted resource utilization and a set of power utilizationcriteria.
 2. The method of claim 1, wherein predicting resourceutilization further comprises: measuring resource utilization on thedevice for a given number of requests executed by the device; andobtaining correlation information between the measured resourceutilization and the number of requests executed by the device.
 3. Themethod of claim 1, further comprises: determining an overload thresholdfor the subset of devices; and determining if all power-on devices inthe subset of devices are above the overload threshold; selecting adevice from the subset of devices to power on if all power-on devices inthe subset of are above the overload threshold; selecting a device fromthe subset of devices to power off if all power-on devices in the subsetof are below the overload threshold.
 4. The method of claim 3, whereindetermining an overload threshold can be determined dynamically or canbe pre-defined.
 5. The method of claim 3, wherein selecting a device topower off further comprises: identifying a device, if any, to power offbased on the set of power utilization criteria; marking the identifieddevice as unavailable; updating state information for the identifieddevice; and initiating shut down for the identified device.
 6. Themethod of claim 3, wherein selecting a device to power on furthercomprises: identifying a device, if any, to power on based on the set ofpower utilization criteria; updating state information for theidentified device; and initiating power-on for the identified device. 7.The method of claim 1, wherein the set of power utilization criteriacomprises: lowest operating frequency; highest power consumption;maximum post-utilization; lowest power consumption; highest operatingfrequency; shortest resume time; and application capabilities.
 8. Themethod of claim 1, wherein the set of power utilization criteriaprovides high power savings without significant loss in performance. 9.The method of claim 1, wherein the subset of devices are logicallygrouped to provide a common set of functionality.
 10. The method ofclaim 1, further comprising: predicting usage demand associated with thesubset of devices; and using the predicted demand to augment selectionof a candidate device from the subset of devices for powering on or off.11. The method of claim 10, further comprising analyzing historicalusage data associated with the subset of devices.
 12. The method ofclaim 10, further comprising moving devices from the subset of devicesto one or more other subsets of devices to optimize the power managementacross the subsets of devices.
 13. The method of claim 3, whereindetermining if all power-on devices in the subset of devices are abovethe overload threshold is performed at periodic intervals.
 14. A systemfor power management of a subset of devices, the system comprising: atleast one application delivery controller for receiving requests fromclient devices; at least one power management machine for predictingresource utilization associated with each device from the subset ofdevices, wherein the subset of devices do not include customizedsoftware, firmware or hardware for predicting the resource utilization.15. The system of claim 14, wherein the least one power managementmachine interacts with the application delivery controller to obtaininformation on the total number of requests received by the applicationcontroller and the number of requests executed by each device in thesubset of devices.
 16. The system of claim 14, wherein the least onepower management machine remotely queries each device for information onthe number of requests and resource utilization information in thesubset of devices.
 17. The system of claim 14, further comprising atleast one database server for storing correlation information thatcorrelates the number of requests executed by each device and theresource utilization information associated with the execution of therequests for each device.
 18. The system of claim 14, wherein the atleast one power management machine is used for: determining an overloadthreshold for the subset of devices; and determining if all power-ondevices in the subset of devices are above the overload threshold;selecting a device from the subset of devices to power on if allpower-on devices in the subset of are above the overload threshold;selecting a device from the subset of devices to power off if allpower-on devices in the subset of are below the overload threshold. 19.The system of claim 14, wherein the at least one power managementmachine uses a set of power utilization criteria for selecting a devicefrom the subset of devices to power on or off, the power utilizationcriteria: lowest operating frequency; highest power consumption; maximumpost-utilization; lowest power consumption; highest operating frequency;shortest resume time; and application capabilities.